Overview

Dataset statistics

Number of variables20
Number of observations28132
Missing cells10820
Missing cells (%)1.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.2 MiB
Average record size in memory269.8 B

Variable types

NUM17
CAT2
DATE1

Warnings

Code provincia alpha has a high cardinality: 52 distinct values High cardinality
value__tmed__min is highly correlated with value__tmed__mean and 4 other fieldsHigh correlation
value__tmed__mean is highly correlated with value__tmed__min and 7 other fieldsHigh correlation
value__tmed__max is highly correlated with value__tmed__mean and 5 other fieldsHigh correlation
value__prec__max is highly correlated with value__prec__mean and 1 other fieldsHigh correlation
value__prec__mean is highly correlated with value__prec__maxHigh correlation
value__prec__std is highly correlated with value__prec__maxHigh correlation
value__tmin__mean is highly correlated with value__tmed__mean and 3 other fieldsHigh correlation
value__tmin__min is highly correlated with value__tmed__mean and 2 other fieldsHigh correlation
value__tmin__max is highly correlated with value__tmed__mean and 2 other fieldsHigh correlation
value__tmax__mean is highly correlated with value__tmed__mean and 4 other fieldsHigh correlation
value__tmax__min is highly correlated with value__tmed__mean and 3 other fieldsHigh correlation
value__tmax__max is highly correlated with value__tmed__mean and 3 other fieldsHigh correlation
Code comunidad autónoma alpha is highly correlated with Code provincia alphaHigh correlation
Code provincia alpha is highly correlated with Code comunidad autónoma alphaHigh correlation
value__tmed__std has 2705 (9.6%) missing values Missing
value__prec__std has 2705 (9.6%) missing values Missing
value__tmin__std has 2705 (9.6%) missing values Missing
value__tmax__std has 2705 (9.6%) missing values Missing
Code provincia alpha is uniformly distributed Uniform
value__prec__sum has 14063 (50.0%) zeros Zeros
value__prec__mean has 13878 (49.3%) zeros Zeros
value__prec__std has 12120 (43.1%) zeros Zeros
value__prec__min has 21078 (74.9%) zeros Zeros
value__prec__max has 13878 (49.3%) zeros Zeros

Reproduction

Analysis started2021-07-17 17:56:07.659416
Analysis finished2021-07-17 17:56:41.383740
Duration33.72 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

fecha
Date

Distinct541
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size439.6 KiB
Minimum2020-01-01 00:00:00
Maximum2021-06-24 00:00:00
2021-07-17T19:56:41.441740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:41.561741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Code provincia alpha
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM

Distinct52
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size439.6 KiB
V
 
541
SG
 
541
C
 
541
AB
 
541
CU
 
541
Other values (47)
25427 
ValueCountFrequency (%) 
V5411.9%
 
SG5411.9%
 
C5411.9%
 
AB5411.9%
 
CU5411.9%
 
M5411.9%
 
BA5411.9%
 
LE5411.9%
 
CA5411.9%
 
PM5411.9%
 
ML5411.9%
 
BI5411.9%
 
CR5411.9%
 
SE5411.9%
 
LO5411.9%
 
SO5411.9%
 
CE5411.9%
 
AV5411.9%
 
S5411.9%
 
T5411.9%
 
SA5411.9%
 
A5411.9%
 
VI5411.9%
 
Z5411.9%
 
TF5411.9%
 
Other values (27)1460751.9%
 
2021-07-17T19:56:41.690741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-07-17T19:56:41.797740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.75
Min length1

Overview of Unicode Properties

Unique unicode characters20
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
A595112.1%
 
C541011.0%
 
S43288.8%
 
O37877.7%
 
L32466.6%
 
U32466.6%
 
B27055.5%
 
G27055.5%
 
M27055.5%
 
V21644.4%
 
E21644.4%
 
T21644.4%
 
I16233.3%
 
R16233.3%
 
P16233.3%
 
H10822.2%
 
Z10822.2%
 
J5411.1%
 
N5411.1%
 
F5411.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter49231100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A595112.1%
 
C541011.0%
 
S43288.8%
 
O37877.7%
 
L32466.6%
 
U32466.6%
 
B27055.5%
 
G27055.5%
 
M27055.5%
 
V21644.4%
 
E21644.4%
 
T21644.4%
 
I16233.3%
 
R16233.3%
 
P16233.3%
 
H10822.2%
 
Z10822.2%
 
J5411.1%
 
N5411.1%
 
F5411.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin49231100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A595112.1%
 
C541011.0%
 
S43288.8%
 
O37877.7%
 
L32466.6%
 
U32466.6%
 
B27055.5%
 
G27055.5%
 
M27055.5%
 
V21644.4%
 
E21644.4%
 
T21644.4%
 
I16233.3%
 
R16233.3%
 
P16233.3%
 
H10822.2%
 
Z10822.2%
 
J5411.1%
 
N5411.1%
 
F5411.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII49231100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
A595112.1%
 
C541011.0%
 
S43288.8%
 
O37877.7%
 
L32466.6%
 
U32466.6%
 
B27055.5%
 
G27055.5%
 
M27055.5%
 
V21644.4%
 
E21644.4%
 
T21644.4%
 
I16233.3%
 
R16233.3%
 
P16233.3%
 
H10822.2%
 
Z10822.2%
 
J5411.1%
 
N5411.1%
 
F5411.1%
 

value__tmed__mean
Real number (ℝ)

HIGH CORRELATION

Distinct10277
Distinct (%)36.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.68626326
Minimum-7.8
Maximum35.4
Zeros2
Zeros (%)< 0.1%
Memory size439.6 KiB
2021-07-17T19:56:41.905740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-7.8
5-th percentile4.5
Q110.1
median14.1
Q319.38472222
95-th percentile25.85
Maximum35.4
Range43.2
Interquartile range (IQR)9.284722222

Descriptive statistics

Standard deviation6.46667032
Coefficient of variation (CV)0.4403210131
Kurtosis-0.4431002291
Mean14.68626326
Median Absolute Deviation (MAD)4.54
Skewness0.1342741406
Sum413153.9582
Variance41.81782502
MonotocityNot monotonic
2021-07-17T19:56:42.019740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
13730.3%
 
15670.2%
 
11.6600.2%
 
16.2570.2%
 
11560.2%
 
12550.2%
 
10540.2%
 
8.6540.2%
 
14.4540.2%
 
16.4540.2%
 
14.6540.2%
 
17530.2%
 
10.4530.2%
 
12.8530.2%
 
12.4530.2%
 
9530.2%
 
10.5510.2%
 
10.2510.2%
 
13.4510.2%
 
14510.2%
 
15.8500.2%
 
11.5500.2%
 
10.8470.2%
 
18.8470.2%
 
19.2460.2%
 
Other values (10252)2678595.2%
 
ValueCountFrequency (%) 
-7.81< 0.1%
 
-6.31< 0.1%
 
-6.21< 0.1%
 
-6.1251< 0.1%
 
-61< 0.1%
 
-5.9751< 0.1%
 
-5.751< 0.1%
 
-5.5251< 0.1%
 
-5.31< 0.1%
 
-5.2251< 0.1%
 
ValueCountFrequency (%) 
35.41< 0.1%
 
32.81< 0.1%
 
32.71< 0.1%
 
32.5751< 0.1%
 
32.5251< 0.1%
 
32.4251< 0.1%
 
32.351< 0.1%
 
32.31< 0.1%
 
32.31< 0.1%
 
32.251< 0.1%
 

value__tmed__std
Real number (ℝ≥0)

MISSING

Distinct20820
Distinct (%)81.9%
Missing2705
Missing (%)9.6%
Infinite0
Infinite (%)0.0%
Mean1.768801373
Minimum0
Maximum7.990306627
Zeros95
Zeros (%)0.3%
Memory size439.6 KiB
2021-07-17T19:56:42.145741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3953475238
Q11.004572878
median1.551773179
Q32.273197871
95-th percentile4.007883897
Maximum7.990306627
Range7.990306627
Interquartile range (IQR)1.268624994

Descriptive statistics

Standard deviation1.092408598
Coefficient of variation (CV)0.6175982305
Kurtosis1.674067433
Mean1.768801373
Median Absolute Deviation (MAD)0.617492661
Skewness1.173941872
Sum44975.31252
Variance1.193356545
MonotocityNot monotonic
2021-07-17T19:56:42.266742image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.70710678121060.4%
 
0950.3%
 
0.3535533906780.3%
 
1.414213562590.2%
 
0.1414213562570.2%
 
1.060660172560.2%
 
0.5656854249390.1%
 
0.2828427125380.1%
 
0.2828427125360.1%
 
0.2121320344350.1%
 
0.4242640687290.1%
 
0.2828427125260.1%
 
0.4242640687260.1%
 
0.4242640687260.1%
 
0.9899494937250.1%
 
0.07071067812240.1%
 
1.272792206230.1%
 
0.8485281374230.1%
 
2.121320344220.1%
 
0.4242640687220.1%
 
0.1414213562220.1%
 
0.07071067812210.1%
 
0.2828427125210.1%
 
1.767766953200.1%
 
1.555634919190.1%
 
Other values (20795)2447987.0%
 
(Missing)27059.6%
 
ValueCountFrequency (%) 
0950.3%
 
0.023570226042< 0.1%
 
0.035355339061< 0.1%
 
0.035355339063< 0.1%
 
0.035355339061< 0.1%
 
0.040406101781< 0.1%
 
0.047140452082< 0.1%
 
0.047140452081< 0.1%
 
0.047140452081< 0.1%
 
0.051< 0.1%
 
ValueCountFrequency (%) 
7.9903066271< 0.1%
 
7.7781745931< 0.1%
 
7.5962710151< 0.1%
 
7.5119904151< 0.1%
 
7.4953318811< 0.1%
 
7.3938600651< 0.1%
 
7.3654033611< 0.1%
 
7.3539105242< 0.1%
 
7.2831998461< 0.1%
 
7.2831998461< 0.1%
 

value__tmed__min
Real number (ℝ)

HIGH CORRELATION

Distinct2205
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.62348393
Minimum-15.2
Maximum35.4
Zeros47
Zeros (%)0.2%
Memory size439.6 KiB
2021-07-17T19:56:42.383739image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-15.2
5-th percentile2
Q17.879166667
median12
Q317.6
95-th percentile24.4
Maximum35.4
Range50.6
Interquartile range (IQR)9.720833333

Descriptive statistics

Standard deviation6.832133445
Coefficient of variation (CV)0.5412240774
Kurtosis-0.3837962977
Mean12.62348393
Median Absolute Deviation (MAD)4.8
Skewness0.1142410977
Sum355123.85
Variance46.67804742
MonotocityNot monotonic
2021-07-17T19:56:42.501741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
11.42470.9%
 
11.62440.9%
 
10.62290.8%
 
112260.8%
 
10.42250.8%
 
132230.8%
 
9.22210.8%
 
10.82180.8%
 
92170.8%
 
8.22160.8%
 
102150.8%
 
11.22140.8%
 
9.62140.8%
 
9.42130.8%
 
72110.8%
 
12.62040.7%
 
8.82000.7%
 
12.21990.7%
 
10.21970.7%
 
13.81960.7%
 
9.81950.7%
 
8.41940.7%
 
8.61940.7%
 
7.61920.7%
 
121920.7%
 
Other values (2180)2283681.2%
 
ValueCountFrequency (%) 
-15.21< 0.1%
 
-13.31< 0.1%
 
-12.31< 0.1%
 
-11.81< 0.1%
 
-10.81< 0.1%
 
-10.41< 0.1%
 
-9.41< 0.1%
 
-9.31< 0.1%
 
-9.11< 0.1%
 
-91< 0.1%
 
ValueCountFrequency (%) 
35.41< 0.1%
 
32.81< 0.1%
 
32.31< 0.1%
 
32.22< 0.1%
 
31.91< 0.1%
 
31.81< 0.1%
 
31.61< 0.1%
 
31.43< 0.1%
 
31.31< 0.1%
 
31.22< 0.1%
 

value__tmed__max
Real number (ℝ)

HIGH CORRELATION

Distinct2070
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.22253839
Minimum-6.2
Maximum35.4
Zeros6
Zeros (%)< 0.1%
Memory size439.6 KiB
2021-07-17T19:56:42.618741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-6.2
5-th percentile6
Q111.6
median15.6
Q320.9
95-th percentile27.4
Maximum35.4
Range41.6
Interquartile range (IQR)9.3

Descriptive statistics

Standard deviation6.49592884
Coefficient of variation (CV)0.4004261654
Kurtosis-0.4420963596
Mean16.22253839
Median Absolute Deviation (MAD)4.533333333
Skewness0.1365259204
Sum456372.45
Variance42.1970915
MonotocityNot monotonic
2021-07-17T19:56:42.733741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
14.42510.9%
 
152480.9%
 
12.22430.9%
 
15.62370.8%
 
142350.8%
 
14.62340.8%
 
13.22330.8%
 
12.82200.8%
 
15.42190.8%
 
15.22180.8%
 
12.42170.8%
 
13.82150.8%
 
132140.8%
 
12.62120.8%
 
16.42120.8%
 
11.62120.8%
 
13.62100.7%
 
14.22100.7%
 
15.82100.7%
 
11.42070.7%
 
122050.7%
 
162050.7%
 
13.42040.7%
 
14.81920.7%
 
16.61920.7%
 
Other values (2045)2267780.6%
 
ValueCountFrequency (%) 
-6.21< 0.1%
 
-61< 0.1%
 
-5.651< 0.1%
 
-5.11< 0.1%
 
-4.551< 0.1%
 
-41< 0.1%
 
-3.61< 0.1%
 
-3.451< 0.1%
 
-3.41< 0.1%
 
-3.11< 0.1%
 
ValueCountFrequency (%) 
35.41< 0.1%
 
34.62< 0.1%
 
34.31< 0.1%
 
34.21< 0.1%
 
341< 0.1%
 
33.91< 0.1%
 
33.81< 0.1%
 
33.61< 0.1%
 
33.44< 0.1%
 
33.28< 0.1%
 

value__prec__sum
Real number (ℝ≥0)

ZEROS

Distinct3098
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.987256505
Minimum0
Maximum709.6
Zeros14063
Zeros (%)50.0%
Memory size439.6 KiB
2021-07-17T19:56:42.854741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.01666666667
Q34.2
95-th percentile42.7
Maximum709.6
Range709.6
Interquartile range (IQR)4.2

Descriptive statistics

Standard deviation24.49519829
Coefficient of variation (CV)3.066784981
Kurtosis95.50916376
Mean7.987256505
Median Absolute Deviation (MAD)0.01666666667
Skewness7.481379648
Sum224697.5
Variance600.0147392
MonotocityNot monotonic
2021-07-17T19:56:42.976741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01406350.0%
 
0.28503.0%
 
0.17202.6%
 
0.44131.5%
 
0.81940.7%
 
0.61890.7%
 
0.51740.6%
 
11620.6%
 
0.31540.5%
 
1.61140.4%
 
1.21120.4%
 
0.31020.4%
 
1.41020.4%
 
21000.4%
 
0.7950.3%
 
2.2920.3%
 
0.6800.3%
 
1.5800.3%
 
1.1770.3%
 
1.8750.3%
 
3.2720.3%
 
2.4690.2%
 
3660.2%
 
2.6660.2%
 
4.2650.2%
 
Other values (3073)984635.0%
 
ValueCountFrequency (%) 
01406350.0%
 
0.01666666667220.1%
 
0.01666666667180.1%
 
0.03333333333390.1%
 
0.03333333333180.1%
 
0.03333333333300.1%
 
0.055< 0.1%
 
0.052< 0.1%
 
0.05400.1%
 
0.055< 0.1%
 
ValueCountFrequency (%) 
709.61< 0.1%
 
541.91< 0.1%
 
516.41< 0.1%
 
467.51< 0.1%
 
457.71< 0.1%
 
430.33333331< 0.1%
 
414.41< 0.1%
 
395.11< 0.1%
 
394.31< 0.1%
 
391.91< 0.1%
 

value__prec__mean
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct4743
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.847380057
Minimum0
Maximum134.2
Zeros13878
Zeros (%)49.3%
Memory size439.6 KiB
2021-07-17T19:56:43.110744image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.01
Q31.225
95-th percentile10.23761905
Maximum134.2
Range134.2
Interquartile range (IQR)1.225

Descriptive statistics

Standard deviation4.836977506
Coefficient of variation (CV)2.618290421
Kurtosis56.39655216
Mean1.847380057
Median Absolute Deviation (MAD)0.01
Skewness5.637200281
Sum51970.49575
Variance23.3963514
MonotocityNot monotonic
2021-07-17T19:56:43.237776image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01387849.3%
 
0.052681.0%
 
0.12620.9%
 
0.22340.8%
 
0.0252220.8%
 
0.033333333331810.6%
 
0.066666666671650.6%
 
0.041500.5%
 
0.41460.5%
 
0.021410.5%
 
0.61170.4%
 
0.016666666671140.4%
 
0.13333333331000.4%
 
0.3900.3%
 
0.5840.3%
 
1830.3%
 
0.8650.2%
 
1.2630.2%
 
1.6630.2%
 
0.25620.2%
 
0.08610.2%
 
0.01580.2%
 
0.125550.2%
 
0.2666666667510.2%
 
0.075490.2%
 
Other values (4718)1137040.4%
 
ValueCountFrequency (%) 
01387849.3%
 
0.0015151515151< 0.1%
 
0.0016666666672< 0.1%
 
0.0020833333331< 0.1%
 
0.0023809523811< 0.1%
 
0.0027777777782< 0.1%
 
0.0027777777784< 0.1%
 
0.003030303031< 0.1%
 
0.0033333333332< 0.1%
 
0.0033333333335< 0.1%
 
ValueCountFrequency (%) 
134.21< 0.1%
 
78.844444441< 0.1%
 
74.0251< 0.1%
 
73.771428571< 0.1%
 
73.351< 0.1%
 
65.141< 0.1%
 
63.166666671< 0.1%
 
61.5251< 0.1%
 
61.476190481< 0.1%
 
59.151< 0.1%
 

value__prec__std
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct8793
Distinct (%)34.6%
Missing2705
Missing (%)9.6%
Infinite0
Infinite (%)0.0%
Mean1.324181887
Minimum0
Maximum73.6805266
Zeros12120
Zeros (%)43.1%
Memory size439.6 KiB
2021-07-17T19:56:43.369740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.04082482905
Q31.11835513
95-th percentile6.928734316
Maximum73.6805266
Range73.6805266
Interquartile range (IQR)1.11835513

Descriptive statistics

Standard deviation3.268668411
Coefficient of variation (CV)2.46844368
Kurtosis58.25060854
Mean1.324181887
Median Absolute Deviation (MAD)0.04082482905
Skewness5.799194228
Sum33669.97285
Variance10.68419318
MonotocityNot monotonic
2021-07-17T19:56:43.513740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01212043.1%
 
0.12440.9%
 
0.052110.8%
 
0.11547005381440.5%
 
0.08944271911120.4%
 
0.14142135621050.4%
 
0.057735026921030.4%
 
0.044721359551020.4%
 
0.2910.3%
 
0.04082482905680.2%
 
0.2309401077500.2%
 
0.07071067812500.2%
 
0.08164965809480.2%
 
0.09574271078470.2%
 
0.3535533906440.2%
 
0.4410.1%
 
0.1788854382390.1%
 
0.0377964473380.1%
 
0.0316227766360.1%
 
0.2828427125340.1%
 
0.03333333333340.1%
 
0.2828427125340.1%
 
0.3464101615330.1%
 
0.0894427191320.1%
 
0.15310.1%
 
Other values (8768)1153641.0%
 
(Missing)27059.6%
 
ValueCountFrequency (%) 
01212043.1%
 
0.0024382992451< 0.1%
 
0.0048765984911< 0.1%
 
0.0050251890761< 0.1%
 
0.0052704627672< 0.1%
 
0.005892556511< 0.1%
 
0.0062994078831< 0.1%
 
0.0064874912011< 0.1%
 
0.0068041381744< 0.1%
 
0.0068041381741< 0.1%
 
ValueCountFrequency (%) 
73.68052661< 0.1%
 
64.770981161< 0.1%
 
61.501360511< 0.1%
 
57.747424181< 0.1%
 
54.970924131< 0.1%
 
52.947291851< 0.1%
 
49.322194421< 0.1%
 
47.616723751< 0.1%
 
43.560102161< 0.1%
 
42.885413221< 0.1%
 

value__prec__min
Real number (ℝ≥0)

ZEROS

Distinct813
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8372956064
Minimum0
Maximum134.2
Zeros21078
Zeros (%)74.9%
Memory size439.6 KiB
2021-07-17T19:56:43.674778image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.02976190476
95-th percentile5
Maximum134.2
Range134.2
Interquartile range (IQR)0.02976190476

Descriptive statistics

Standard deviation3.089758114
Coefficient of variation (CV)3.690164012
Kurtosis180.1340599
Mean0.8372956064
Median Absolute Deviation (MAD)0
Skewness8.984302494
Sum23554.8
Variance9.546605205
MonotocityNot monotonic
2021-07-17T19:56:43.820740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
02107874.9%
 
0.26342.3%
 
0.43601.3%
 
0.13251.2%
 
0.62350.8%
 
0.81960.7%
 
11960.7%
 
0.31420.5%
 
1.21330.5%
 
1.61230.4%
 
0.51140.4%
 
21100.4%
 
1.41060.4%
 
1.8910.3%
 
2.2900.3%
 
0.7870.3%
 
3770.3%
 
2.6740.3%
 
2.4700.2%
 
2.8640.2%
 
4.2570.2%
 
1.5570.2%
 
3.2560.2%
 
5550.2%
 
0.9550.2%
 
Other values (788)354712.6%
 
ValueCountFrequency (%) 
02107874.9%
 
0.016666666676< 0.1%
 
0.0166666666712< 0.1%
 
0.016666666671< 0.1%
 
0.0251< 0.1%
 
0.028571428571< 0.1%
 
0.03333333333230.1%
 
0.0333333333312< 0.1%
 
0.03333333333250.1%
 
0.033333333331< 0.1%
 
ValueCountFrequency (%) 
134.21< 0.1%
 
58.81< 0.1%
 
58.21< 0.1%
 
53.31< 0.1%
 
49.366666671< 0.1%
 
47.61< 0.1%
 
45.61< 0.1%
 
44.61< 0.1%
 
43.11< 0.1%
 
42.71< 0.1%
 

value__prec__max
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1677
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.460870183
Minimum0
Maximum177.8
Zeros13878
Zeros (%)49.3%
Memory size439.6 KiB
2021-07-17T19:56:43.958775image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.05
Q32.8
95-th percentile18.6
Maximum177.8
Range177.8
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation8.605465916
Coefficient of variation (CV)2.486503526
Kurtosis48.44477366
Mean3.460870183
Median Absolute Deviation (MAD)0.05
Skewness5.401623613
Sum97361.2
Variance74.05404363
MonotocityNot monotonic
2021-07-17T19:56:44.077740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01387849.3%
 
0.210803.8%
 
0.18142.9%
 
0.44761.7%
 
0.63361.2%
 
0.82741.0%
 
12210.8%
 
1.22060.7%
 
1.61930.7%
 
0.31910.7%
 
1.41860.7%
 
0.51540.5%
 
21510.5%
 
2.21450.5%
 
1.81420.5%
 
2.61300.5%
 
2.41260.4%
 
3.21150.4%
 
31140.4%
 
0.71040.4%
 
41010.4%
 
2.8990.4%
 
5.8880.3%
 
4.8870.3%
 
0.9860.3%
 
Other values (1652)863530.7%
 
ValueCountFrequency (%) 
01387849.3%
 
0.01666666667230.1%
 
0.01666666667230.1%
 
0.016666666671< 0.1%
 
0.0252< 0.1%
 
0.028571428571< 0.1%
 
0.03333333333460.2%
 
0.03333333333230.1%
 
0.03333333333340.1%
 
0.033333333331< 0.1%
 
ValueCountFrequency (%) 
177.81< 0.1%
 
165.81< 0.1%
 
1501< 0.1%
 
140.61< 0.1%
 
138.16666671< 0.1%
 
134.21< 0.1%
 
129.81< 0.1%
 
1241< 0.1%
 
113.21< 0.1%
 
110.53333331< 0.1%
 

value__tmin__mean
Real number (ℝ)

HIGH CORRELATION

Distinct10208
Distinct (%)36.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.336979066
Minimum-17.53333333
Maximum31.8
Zeros18
Zeros (%)0.1%
Memory size439.6 KiB
2021-07-17T19:56:44.205740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-17.53333333
5-th percentile-0.6666666667
Q14.975
median9.175
Q313.8
95-th percentile19.73333333
Maximum31.8
Range49.33333333
Interquartile range (IQR)8.825

Descriptive statistics

Standard deviation6.154699084
Coefficient of variation (CV)0.6591745618
Kurtosis-0.4499099234
Mean9.336979066
Median Absolute Deviation (MAD)4.419949495
Skewness7.249209527e-05
Sum262667.8951
Variance37.88032082
MonotocityNot monotonic
2021-07-17T19:56:44.337740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
6.5530.2%
 
12510.2%
 
10.3510.2%
 
8.5500.2%
 
7.3490.2%
 
5.8490.2%
 
7480.2%
 
10470.2%
 
10.1470.2%
 
8.4460.2%
 
6.7460.2%
 
7.9450.2%
 
13450.2%
 
10.5440.2%
 
9.2440.2%
 
4.5440.2%
 
11.4430.2%
 
7.1430.2%
 
9430.2%
 
11.5430.2%
 
10.4430.2%
 
8.1420.1%
 
12.5420.1%
 
6.3420.1%
 
10.2420.1%
 
Other values (10183)2699095.9%
 
ValueCountFrequency (%) 
-17.533333331< 0.1%
 
-14.233333331< 0.1%
 
-13.666666671< 0.1%
 
-13.61< 0.1%
 
-13.1751< 0.1%
 
-11.751< 0.1%
 
-11.351< 0.1%
 
-10.81< 0.1%
 
-10.4751< 0.1%
 
-9.951< 0.1%
 
ValueCountFrequency (%) 
31.81< 0.1%
 
27.61< 0.1%
 
27.41< 0.1%
 
26.41< 0.1%
 
26.21< 0.1%
 
25.3251< 0.1%
 
25.31< 0.1%
 
25.21< 0.1%
 
252< 0.1%
 
24.921< 0.1%
 

value__tmin__std
Real number (ℝ≥0)

MISSING

Distinct20531
Distinct (%)80.7%
Missing2705
Missing (%)9.6%
Infinite0
Infinite (%)0.0%
Mean2.093874788
Minimum0
Maximum10.10428952
Zeros52
Zeros (%)0.2%
Memory size439.6 KiB
2021-07-17T19:56:44.469771image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4949747468
Q11.272792206
median1.974335331
Q32.748791942
95-th percentile4.19150813
Maximum10.10428952
Range10.10428952
Interquartile range (IQR)1.475999736

Descriptive statistics

Standard deviation1.133405327
Coefficient of variation (CV)0.541295656
Kurtosis1.27369621
Mean2.093874788
Median Absolute Deviation (MAD)0.7342928579
Skewness0.8184927671
Sum53240.95424
Variance1.284607635
MonotocityNot monotonic
2021-07-17T19:56:44.589741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.3535533906900.3%
 
0.7071067812830.3%
 
0520.2%
 
1.060660172450.2%
 
1.414213562430.2%
 
0.1414213562410.1%
 
1.767766953340.1%
 
0.2121320344320.1%
 
0.5656854249280.1%
 
2.121320344270.1%
 
0.2828427125270.1%
 
0.5656854249250.1%
 
0.2828427125250.1%
 
0.4949747468240.1%
 
0.9192388155240.1%
 
0.8485281374230.1%
 
0.07071067812210.1%
 
1.272792206210.1%
 
0.4242640687210.1%
 
0.7778174593210.1%
 
1.202081528200.1%
 
1.202081528190.1%
 
2.333452378190.1%
 
0.4949747468190.1%
 
0.4242640687180.1%
 
Other values (20506)2462587.5%
 
(Missing)27059.6%
 
ValueCountFrequency (%) 
0520.2%
 
0.040406101781< 0.1%
 
0.047140452081< 0.1%
 
0.057735026922< 0.1%
 
0.057735026921< 0.1%
 
0.057735026921< 0.1%
 
0.070710678124< 0.1%
 
0.07071067812170.1%
 
0.0707106781210< 0.1%
 
0.070710678123< 0.1%
 
ValueCountFrequency (%) 
10.104289521< 0.1%
 
10.089499491< 0.1%
 
10.059671961< 0.1%
 
9.6472362191< 0.1%
 
9.5709194961< 0.1%
 
9.2906314821< 0.1%
 
8.9907174351< 0.1%
 
8.4665813641< 0.1%
 
8.4389474071< 0.1%
 
8.2976760951< 0.1%
 

value__tmin__min
Real number (ℝ)

HIGH CORRELATION

Distinct2433
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.931158467
Minimum-25.2
Maximum31.8
Zeros124
Zeros (%)0.4%
Memory size439.6 KiB
2021-07-17T19:56:44.705774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-25.2
5-th percentile-3
Q12.3
median6.7
Q311.5
95-th percentile17.6
Maximum31.8
Range57
Interquartile range (IQR)9.2

Descriptive statistics

Standard deviation6.364344529
Coefficient of variation (CV)0.9182223374
Kurtosis-0.3055577776
Mean6.931158467
Median Absolute Deviation (MAD)4.6
Skewness0.04894972211
Sum194987.35
Variance40.50488128
MonotocityNot monotonic
2021-07-17T19:56:44.816789image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
4.51620.6%
 
6.71590.6%
 
4.31570.6%
 
7.21540.5%
 
5.71520.5%
 
2.61510.5%
 
6.61510.5%
 
7.61500.5%
 
6.31500.5%
 
5.31490.5%
 
51480.5%
 
5.21450.5%
 
3.81450.5%
 
91440.5%
 
3.31440.5%
 
7.31440.5%
 
6.41430.5%
 
5.81420.5%
 
2.11420.5%
 
6.21410.5%
 
6.51410.5%
 
4.21400.5%
 
6.11400.5%
 
9.41390.5%
 
3.91390.5%
 
Other values (2408)2446086.9%
 
ValueCountFrequency (%) 
-25.21< 0.1%
 
-221< 0.1%
 
-21.61< 0.1%
 
-21.31< 0.1%
 
-19.91< 0.1%
 
-19.11< 0.1%
 
-18.51< 0.1%
 
-181< 0.1%
 
-17.41< 0.1%
 
-16.51< 0.1%
 
ValueCountFrequency (%) 
31.81< 0.1%
 
27.61< 0.1%
 
27.41< 0.1%
 
26.41< 0.1%
 
26.21< 0.1%
 
25.31< 0.1%
 
25.21< 0.1%
 
252< 0.1%
 
24.91< 0.1%
 
24.83< 0.1%
 

value__tmin__max
Real number (ℝ)

HIGH CORRELATION

Distinct2183
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.22898656
Minimum-10.6
Maximum31.8
Zeros50
Zeros (%)0.2%
Memory size439.6 KiB
2021-07-17T19:56:44.937773image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-10.6
5-th percentile0.9
Q16.9
median11.1
Q315.7
95-th percentile21.7
Maximum31.8
Range42.4
Interquartile range (IQR)8.8

Descriptive statistics

Standard deviation6.206698536
Coefficient of variation (CV)0.5527389761
Kurtosis-0.4513249714
Mean11.22898656
Median Absolute Deviation (MAD)4.4
Skewness-0.02440409222
Sum315893.85
Variance38.52310671
MonotocityNot monotonic
2021-07-17T19:56:45.053742image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
10.21660.6%
 
101640.6%
 
111630.6%
 
10.31610.6%
 
9.61600.6%
 
9.91590.6%
 
11.41590.6%
 
11.21570.6%
 
8.51570.6%
 
91570.6%
 
10.61550.6%
 
8.61530.5%
 
9.81530.5%
 
11.51510.5%
 
12.51510.5%
 
9.11500.5%
 
9.31490.5%
 
10.81490.5%
 
8.41480.5%
 
10.51480.5%
 
8.21470.5%
 
10.11470.5%
 
121470.5%
 
14.61460.5%
 
12.21460.5%
 
Other values (2158)2428986.3%
 
ValueCountFrequency (%) 
-10.61< 0.1%
 
-9.51< 0.1%
 
-92< 0.1%
 
-8.91< 0.1%
 
-8.51< 0.1%
 
-81< 0.1%
 
-7.81< 0.1%
 
-7.72< 0.1%
 
-7.61< 0.1%
 
-7.41< 0.1%
 
ValueCountFrequency (%) 
31.81< 0.1%
 
291< 0.1%
 
27.81< 0.1%
 
27.61< 0.1%
 
27.51< 0.1%
 
27.41< 0.1%
 
27.23< 0.1%
 
27.11< 0.1%
 
272< 0.1%
 
26.92< 0.1%
 

value__tmax__mean
Real number (ℝ)

HIGH CORRELATION

Distinct10800
Distinct (%)38.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.03541409
Minimum-5.1
Maximum42.275
Zeros0
Zeros (%)0.0%
Memory size439.6 KiB
2021-07-17T19:56:45.175739image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-5.1
5-th percentile8.7
Q114.95
median19.2
Q325.06666667
95-th percentile33.24236111
Maximum42.275
Range47.375
Interquartile range (IQR)10.11666667

Descriptive statistics

Standard deviation7.386518929
Coefficient of variation (CV)0.3686731353
Kurtosis-0.2869356514
Mean20.03541409
Median Absolute Deviation (MAD)4.886607143
Skewness0.2655169116
Sum563636.2692
Variance54.5606619
MonotocityNot monotonic
2021-07-17T19:56:45.721739image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
17.5560.2%
 
16520.2%
 
16.3510.2%
 
18.2500.2%
 
15.4500.2%
 
21.1490.2%
 
17490.2%
 
19490.2%
 
18.8490.2%
 
16.1480.2%
 
16.5470.2%
 
18470.2%
 
16.8460.2%
 
15460.2%
 
18.5460.2%
 
17.7450.2%
 
19.3450.2%
 
20.3450.2%
 
14.5440.2%
 
17.8440.2%
 
16.6430.2%
 
16.4430.2%
 
17.4420.1%
 
14.9420.1%
 
21420.1%
 
Other values (10775)2696295.8%
 
ValueCountFrequency (%) 
-5.11< 0.1%
 
-4.3916666671< 0.1%
 
-4.21< 0.1%
 
-3.6833333331< 0.1%
 
-3.051< 0.1%
 
-2.9751< 0.1%
 
-2.2666666671< 0.1%
 
-1.5583333331< 0.1%
 
-0.91< 0.1%
 
-0.851< 0.1%
 
ValueCountFrequency (%) 
42.2751< 0.1%
 
42.181< 0.1%
 
42.11< 0.1%
 
41.981< 0.1%
 
41.833333331< 0.1%
 
41.8251< 0.1%
 
41.583333331< 0.1%
 
41.42< 0.1%
 
41.321< 0.1%
 
41.0751< 0.1%
 

value__tmax__std
Real number (ℝ≥0)

MISSING

Distinct21636
Distinct (%)85.1%
Missing2705
Missing (%)9.6%
Infinite0
Infinite (%)0.0%
Mean2.058930361
Minimum0
Maximum9.40452019
Zeros58
Zeros (%)0.2%
Memory size439.6 KiB
2021-07-17T19:56:45.851739image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4509249753
Q11.158375297
median1.790969751
Q32.626785107
95-th percentile4.68598975
Maximum9.40452019
Range9.40452019
Interquartile range (IQR)1.468409811

Descriptive statistics

Standard deviation1.28441385
Coefficient of variation (CV)0.6238257854
Kurtosis1.660507931
Mean2.058930361
Median Absolute Deviation (MAD)0.7106920092
Skewness1.186150721
Sum52352.42229
Variance1.649718937
MonotocityNot monotonic
2021-07-17T19:56:45.976739image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.3535533906880.3%
 
0.7071067812790.3%
 
0580.2%
 
1.060660172560.2%
 
0.07071067812500.2%
 
1.414213562460.2%
 
0.4242640687410.1%
 
0.7778174593380.1%
 
0.1414213562370.1%
 
0.2828427125370.1%
 
0.1414213562300.1%
 
0.2121320344280.1%
 
1.767766953270.1%
 
0.2121320344270.1%
 
0.6363961031260.1%
 
0.2121320344240.1%
 
0.9899494937230.1%
 
3.181980515220.1%
 
1.343502884210.1%
 
1.13137085200.1%
 
0.07071067812200.1%
 
0.5656854249200.1%
 
0.4949747468200.1%
 
0.2828427125190.1%
 
0.5656854249180.1%
 
Other values (21611)2455287.3%
 
(Missing)27059.6%
 
ValueCountFrequency (%) 
0580.2%
 
0.035355339061< 0.1%
 
0.035355339062< 0.1%
 
0.040406101781< 0.1%
 
0.047140452081< 0.1%
 
0.051< 0.1%
 
0.057735026921< 0.1%
 
0.057735026921< 0.1%
 
0.057735026923< 0.1%
 
0.057735026923< 0.1%
 
ValueCountFrequency (%) 
9.404520191< 0.1%
 
9.2381455571< 0.1%
 
8.9802561211< 0.1%
 
8.9802561211< 0.1%
 
8.9095454431< 0.1%
 
8.9095454431< 0.1%
 
8.7271129251< 0.1%
 
8.6622745281< 0.1%
 
8.4923494981< 0.1%
 
8.4701436431< 0.1%
 

value__tmax__min
Real number (ℝ)

HIGH CORRELATION

Distinct2442
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.67816188
Minimum-7.9
Maximum41.5
Zeros10
Zeros (%)< 0.1%
Memory size439.6 KiB
2021-07-17T19:56:46.097739image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-7.9
5-th percentile5.3
Q112.4
median17
Q322.9
95-th percentile31.5
Maximum41.5
Range49.4
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation7.845268846
Coefficient of variation (CV)0.4437830641
Kurtosis-0.2313347827
Mean17.67816188
Median Absolute Deviation (MAD)5.2
Skewness0.1927046287
Sum497322.05
Variance61.54824326
MonotocityNot monotonic
2021-07-17T19:56:46.219774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
16.51620.6%
 
161510.5%
 
17.41500.5%
 
151470.5%
 
15.41440.5%
 
13.41440.5%
 
15.81430.5%
 
15.51420.5%
 
141400.5%
 
14.81390.5%
 
16.21380.5%
 
16.41360.5%
 
12.91350.5%
 
16.11350.5%
 
13.51340.5%
 
15.21340.5%
 
17.61340.5%
 
16.61340.5%
 
15.31340.5%
 
17.31330.5%
 
15.61330.5%
 
14.61320.5%
 
17.11310.5%
 
16.71310.5%
 
18.31310.5%
 
Other values (2417)2466587.7%
 
ValueCountFrequency (%) 
-7.91< 0.1%
 
-6.72< 0.1%
 
-5.31< 0.1%
 
-5.22< 0.1%
 
-5.11< 0.1%
 
-51< 0.1%
 
-4.72< 0.1%
 
-4.5833333331< 0.1%
 
-4.41< 0.1%
 
-4.3833333331< 0.1%
 
ValueCountFrequency (%) 
41.51< 0.1%
 
40.81< 0.1%
 
40.72< 0.1%
 
40.61< 0.1%
 
40.31< 0.1%
 
40.21< 0.1%
 
403< 0.1%
 
39.95< 0.1%
 
39.81< 0.1%
 
39.73< 0.1%
 

value__tmax__max
Real number (ℝ)

HIGH CORRELATION

Distinct2370
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.90244206
Minimum-5.1
Maximum44.7
Zeros0
Zeros (%)0.0%
Memory size439.6 KiB
2021-07-17T19:56:46.336776image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-5.1
5-th percentile10.5
Q116.6
median21
Q327.1
95-th percentile35.4
Maximum44.7
Range49.8
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation7.53006637
Coefficient of variation (CV)0.34380031
Kurtosis-0.3487820875
Mean21.90244206
Median Absolute Deviation (MAD)5.1
Skewness0.2661881113
Sum616159.5
Variance56.70189954
MonotocityNot monotonic
2021-07-17T19:56:46.455740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
19.21620.6%
 
18.11570.6%
 
18.71540.5%
 
191520.5%
 
18.51510.5%
 
18.31490.5%
 
211480.5%
 
17.61480.5%
 
18.81460.5%
 
16.61450.5%
 
17.81450.5%
 
18.61440.5%
 
171430.5%
 
17.31410.5%
 
181400.5%
 
17.51400.5%
 
16.11380.5%
 
19.41370.5%
 
21.81370.5%
 
19.31360.5%
 
20.81360.5%
 
19.91360.5%
 
17.41350.5%
 
16.91350.5%
 
16.41340.5%
 
Other values (2345)2454387.2%
 
ValueCountFrequency (%) 
-5.11< 0.1%
 
-4.21< 0.1%
 
-4.21< 0.1%
 
-3.31< 0.1%
 
-2.41< 0.1%
 
-1.51< 0.1%
 
-0.61< 0.1%
 
-0.31< 0.1%
 
0.32< 0.1%
 
0.41< 0.1%
 
ValueCountFrequency (%) 
44.71< 0.1%
 
43.93< 0.1%
 
43.81< 0.1%
 
43.52< 0.1%
 
43.31< 0.1%
 
43.22< 0.1%
 
432< 0.1%
 
42.91< 0.1%
 
42.72< 0.1%
 
42.62< 0.1%
 

Code comunidad autónoma alpha
Categorical

HIGH CORRELATION

Distinct19
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size439.6 KiB
CL
4869 
AN
4328 
CM
2705 
GA
2164 
CT
2164 
Other values (14)
11902 
ValueCountFrequency (%) 
CL486917.3%
 
AN432815.4%
 
CM27059.6%
 
GA21647.7%
 
CT21647.7%
 
PV16235.8%
 
VC16235.8%
 
AR16235.8%
 
CN10823.8%
 
EX10823.8%
 
IB5411.9%
 
MC5411.9%
 
MD5411.9%
 
RI5411.9%
 
NC5411.9%
 
ML5411.9%
 
CB5411.9%
 
AS5411.9%
 
CE5411.9%
 
2021-07-17T19:56:46.559740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-07-17T19:56:46.641740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
C1460726.0%
 
A865615.4%
 
N595110.6%
 
L54109.6%
 
M43287.7%
 
V32465.8%
 
T21643.8%
 
G21643.8%
 
R21643.8%
 
E16232.9%
 
P16232.9%
 
X10821.9%
 
I10821.9%
 
B10821.9%
 
D5411.0%
 
S5411.0%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter56264100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C1460726.0%
 
A865615.4%
 
N595110.6%
 
L54109.6%
 
M43287.7%
 
V32465.8%
 
T21643.8%
 
G21643.8%
 
R21643.8%
 
E16232.9%
 
P16232.9%
 
X10821.9%
 
I10821.9%
 
B10821.9%
 
D5411.0%
 
S5411.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin56264100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
C1460726.0%
 
A865615.4%
 
N595110.6%
 
L54109.6%
 
M43287.7%
 
V32465.8%
 
T21643.8%
 
G21643.8%
 
R21643.8%
 
E16232.9%
 
P16232.9%
 
X10821.9%
 
I10821.9%
 
B10821.9%
 
D5411.0%
 
S5411.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII56264100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
C1460726.0%
 
A865615.4%
 
N595110.6%
 
L54109.6%
 
M43287.7%
 
V32465.8%
 
T21643.8%
 
G21643.8%
 
R21643.8%
 
E16232.9%
 
P16232.9%
 
X10821.9%
 
I10821.9%
 
B10821.9%
 
D5411.0%
 
S5411.0%
 

Interactions

2021-07-17T19:56:09.782354image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:09.892388image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:10.007387image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:10.114353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:10.222390image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:10.329389image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:10.438352image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:10.547354image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:10.650386image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:10.759351image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:10.860352image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:11.143385image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:11.243386image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:11.353392image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:11.456354image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:11.558388image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:11.662352image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:11.766352image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:11.876352image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:11.989352image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:12.094386image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:12.203388image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:12.313385image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:12.425390image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:12.531354image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:12.635386image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:12.742383image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:12.842385image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:12.944384image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:13.047353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:13.162355image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:13.268357image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:13.377356image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:13.517353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:13.642388image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:13.754352image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:13.860353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:13.957353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:14.062352image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:14.164353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:14.268353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:14.376353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:14.477353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:14.580353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:14.671353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:14.766353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:14.859353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:14.959352image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:15.052353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:15.146352image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:15.243351image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:15.338351image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:15.451351image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:15.567352image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:15.679351image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:15.790387image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:15.904354image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:16.024353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:16.135356image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:16.243355image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:16.357356image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:16.464356image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:16.576356image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:16.688359image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:16.803356image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:16.912356image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:17.018356image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:17.128356image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:17.235356image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:17.342355image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:17.662355image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:17.761386image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:17.870352image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:17.977385image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:18.093386image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:18.203355image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:18.310385image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:18.420353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:18.528355image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:18.638356image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:18.744355image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:18.856386image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:18.957386image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:19.058394image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:19.162381image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:19.264355image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:19.376388image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:19.490352image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:19.593352image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:19.702352image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:19.813385image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:19.922386image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:20.033353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:20.140386image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:20.252387image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:20.355386image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:20.464353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:20.569389image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:20.680386image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:20.784388image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:20.886386image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:20.992383image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:21.094386image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:21.200387image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:21.308302image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:21.409292image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:21.517302image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:21.622541image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:21.727552image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:21.833123image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:21.937118image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:22.044083image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:22.144117image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:22.245115image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:22.345084image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:22.453084image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:22.553084image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:22.653084image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:22.754084image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:22.858084image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:22.963084image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:23.067084image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:23.163084image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:23.266085image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:23.367084image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:23.474112image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:23.587084image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:23.694084image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:23.800089image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:23.899085image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:24.000089image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:24.101088image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:24.212087image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:24.312087image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:24.426085image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:24.539118image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:24.642123image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:24.750087image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:24.859118image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:24.961084image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:25.078085image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:25.197740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:25.599742image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:25.703778image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:25.807772image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:25.915787image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:26.013777image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:26.115776image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:26.216741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:26.325740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:26.426777image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:26.525775image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:26.625744image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:26.724773image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:26.817743image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:26.913777image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:27.002742image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:27.096740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:27.192785image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:27.297743image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:27.395743image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:27.493744image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:27.593744image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:27.684744image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:27.777771image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:27.866743image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:27.962776image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:28.050740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:28.138773image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:28.239743image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:28.332744image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:28.434739image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:28.539773image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:28.635774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:28.739739image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:28.841740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:28.945740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:29.046740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:29.145772image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:29.247740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:29.342740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:29.439740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:29.534741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:29.637740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:29.733777image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:29.827771image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:29.924741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:30.021740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:30.119774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:30.218741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:30.311740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:30.414742image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:30.525741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:30.633741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:30.736741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:30.837741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:30.940740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:31.034741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:31.131741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:31.226740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:31.328740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:31.424741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:31.518742image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:31.613740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:31.707741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:31.820741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:31.934741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:32.039740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:32.153742image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:32.265741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:32.379741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:32.490741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:32.599742image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:32.710741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:32.813741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:32.922741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:33.028741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:33.141740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:33.246741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:33.350744image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:33.459740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:33.565741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:33.666742image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:33.767742image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:33.861741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:33.960741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:34.059740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:34.162740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:34.261740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:34.357740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:34.452770image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:34.541774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:34.634743image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:34.726774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:34.827776image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:35.248777image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:35.336773image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:35.429773image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:35.520777image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:35.623773image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:35.720774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:35.809774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:35.907781image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:36.002741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:36.099741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:36.193774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:36.287769image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:36.383742image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:36.472774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:36.563774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:36.652777image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:36.749740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:36.838776image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:36.926774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:37.017742image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:37.105773image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:37.203773image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:37.304774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:37.395741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:37.494740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:37.593740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:37.693740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:37.791740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:37.888773image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:37.989741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:38.084741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:38.184741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:38.282741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:38.388741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:38.484740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:38.579740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:38.676740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:38.771741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:38.869741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:38.968742image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:39.059740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:39.159741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:39.257741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:39.356740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:39.453777image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:39.546774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:39.645777image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:39.737769image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:39.832776image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:39.925773image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:40.025773image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:40.117742image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:40.208773image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:40.303770image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-07-17T19:56:46.737740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-07-17T19:56:46.937775image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-07-17T19:56:47.138739image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-07-17T19:56:47.345773image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-07-17T19:56:47.526740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-07-17T19:56:40.543775image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:40.894740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:41.109774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-17T19:56:41.229776image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Sample

First rows

fechaCode provincia alphavalue__tmed__meanvalue__tmed__stdvalue__tmed__minvalue__tmed__maxvalue__prec__sumvalue__prec__meanvalue__prec__stdvalue__prec__minvalue__prec__maxvalue__tmin__meanvalue__tmin__stdvalue__tmin__minvalue__tmin__maxvalue__tmax__meanvalue__tmax__stdvalue__tmax__minvalue__tmax__maxCode comunidad autónoma alpha
02020-01-01A9.5250.9708248.210.30.40.100.2000000.00.43.1252.1515500.25.115.9000000.45460615.316.3VC
12020-01-02A8.6502.8536534.410.40.20.050.1000000.00.24.1752.0056171.35.613.2000003.9183337.616.6VC
22020-01-03A9.2000.3651488.89.60.00.000.0000000.00.01.7251.552149-0.62.616.6500001.14455215.518.2VC
32020-01-04A9.7251.8209437.010.80.00.000.0000000.00.03.7502.817209-0.35.915.6500001.14746114.217.0VC
42020-01-05A9.7501.5864017.410.80.20.050.1000000.00.22.4002.736177-1.35.117.1500001.10000016.218.2VC
52020-01-06A9.0000.9055397.810.00.00.000.0000000.00.01.8252.002290-1.13.316.1250000.61846615.316.6VC
62020-01-07A9.4501.2793237.610.40.00.000.0000000.00.02.7002.677063-1.24.916.2250000.78898715.517.3VC
72020-01-08A8.3750.9535027.19.40.20.050.1000000.00.21.5501.725302-1.02.715.1750000.73200614.616.2VC
82020-01-09A10.4251.3671758.411.40.00.000.0000000.00.04.6001.5853502.35.716.2250001.29711214.417.2VC
92020-01-10A9.5001.8520267.410.912.94.307.3613860.012.83.9002.2538861.35.315.1333331.60104113.516.7VC

Last rows

fechaCode provincia alphavalue__tmed__meanvalue__tmed__stdvalue__tmed__minvalue__tmed__maxvalue__prec__sumvalue__prec__meanvalue__prec__stdvalue__prec__minvalue__prec__maxvalue__tmin__meanvalue__tmin__stdvalue__tmin__minvalue__tmin__maxvalue__tmax__meanvalue__tmax__stdvalue__tmax__minvalue__tmax__maxCode comunidad autónoma alpha
281222021-06-15ZA23.6000001.73205121.624.640.813.6000009.9859906.425.014.2333332.70616611.216.432.9333331.10604431.934.1CL
281232021-06-16ZA20.8333331.40119019.422.21.80.6000000.5291500.21.214.0333332.30289711.516.027.6333330.58594727.228.3CL
281242021-06-17ZA16.4666672.20302814.218.624.88.2666678.6193581.618.012.4000002.4637379.814.720.6000001.95192218.722.6CL
281252021-06-18ZA16.4333331.60727514.617.622.87.6000003.4176014.411.211.1666671.9087529.012.621.7000001.30767020.222.6CL
281262021-06-19ZA14.7333332.27449612.216.613.44.4666670.5033224.05.08.4333332.8536535.210.621.1000001.67032919.322.6CL
281272021-06-20ZA14.2333331.87171912.115.66.22.0666673.0615900.25.610.2666670.9451639.211.018.2000002.85832115.020.5CL
281282021-06-21ZA14.8500001.20208214.015.70.00.0000000.0000000.00.010.4000001.4142149.411.419.2500001.06066018.520.0CL
281292021-06-22ZA16.5333331.50111115.018.00.40.1333330.2309400.00.411.2333331.9553359.213.121.7333331.05039720.722.8CL
281302021-06-23ZA15.8333330.80208115.016.60.00.0000000.0000000.00.09.4000000.7810258.59.922.3000000.96436521.623.4CL
281312021-06-24ZA16.6666671.69213914.818.10.00.0000000.0000000.00.07.4000002.7784894.29.225.9666670.89628925.427.0CL